Automatic driver modeling for integration of human-controlled vehicles into an autonomous vehicle network

ABSTRACT

Automatic driver modeling is used to integrate human-controlled vehicles into an autonomous vehicle network. A driver of a human-controlled vehicle is identified based on behavior patterns of the driver measured by one or more sensors of an autonomous vehicle. A model of the driver is generated based on the behavior patterns of the driver measured by the one or more sensors of the autonomous vehicle. Previously stored behavior patterns of the driver are then retrieved from a database to augment the model of the driver. The model of the driver is then transmitted from the autonomous vehicle to nearby vehicles with autonomous interfaces.

CROSS-REFERENCE TO RELATED APPLICATION

This application is a continuation of U.S. patent application Ser. No.13/962,177, filed Aug. 13, 2013, which in turn claims priority from U.S.Pat. No. 9,361,409, issued Jun. 7, 2016, the entire contents of all ofthe applications listed above are incorporated herein by reference.

BACKGROUND

The present invention relates to autonomous vehicle networks, and morespecifically, to the integration of human-controlled vehicles into anautonomous vehicle network through automatic driver modeling.

As vehicle transportation networks transform from roadways used byvehicles controlled almost entirely by human drivers into roadways forautonomous vehicles controlled wholly by onboard and remote servers, aninterim period will occur when roads must be shared by both autonomousand human-controlled vehicles. Autonomous vehicles rely on a combinationof inputs from onboard sensors, vehicle control computing resources, andcommunications between vehicles, and from remote servers engaged in, forexample, scheduling of vehicle traffic and alerting autonomous vehiclesof conditions that cannot be sensed or communicated locally.

Both interactions and communications between vehicles and with remoteservers require standard autonomous interfaces between autonomousvehicles, their onboard sensors and control mechanisms, and thecomputing resources of other vehicles and remote servers. Theseinterfaces may communicate details such as vehicle location, speed, andsubsequent actions, which then allows other vehicles to plan their ownactions and remote servers to schedule and control the actions of groupsof vehicles effectively. Without these standard autonomous interfaces,communications between vehicles must be through onboard sensors andprocessed locally in the time available, much as a human relies on localbiological sensors to control a vehicle.

SUMMARY

According to an embodiment, a computer-implemented method is providedfor identifying, with a processing device, a driver of ahuman-controlled vehicle based on behavior patterns of the drivermeasured by one or more sensors of an autonomous vehicle. A model of thedriver is generated based on the behavior patterns of the drivermeasured by the one or more sensors of the autonomous vehicle.Previously stored behavior patterns of the driver are then retrievedfrom a database to augment the model of the driver. The model of thedriver is then transmitted from the autonomous vehicle to nearbyvehicles with autonomous interfaces.

According to another embodiment, a computer system is provided foridentifying, with a processing device, a driver of a human-controlledvehicle based on behavior patterns of the driver measured by one or moresensors of an autonomous vehicle. A model of the driver is generatedbased on the behavior patterns of the driver measured by the one or moresensors of the autonomous vehicle. Previously stored behavior patternsof the driver are then retrieved from a database to augment the model ofthe driver. The model of the driver is then transmitted from theautonomous vehicle to nearby vehicles with autonomous interfaces.

According to another embodiment, a computer system is provided formeasuring behavior patterns of a driver of the human-controlled vehicleusing one or more sensors coupled to the processor onboard thehuman-controlled vehicle. A dynamically updated model of the driver isgenerated using the behavior patterns measured by the one or moresensors. An identification of the driver and the model of the driver aretransmitted to nearby vehicles with autonomous interfaces and to aremote server to store the generated model.

According to another embodiment, a method is provided for equipping ahuman-controlled vehicle with an onboard processor, the onboardprocessor executing a computer-implemented method comprising measuring,with a processing device, behavior patterns of a driver of thehuman-controlled vehicle using one or more sensors coupled to theonboard processor. A dynamically updated model of the driver isgenerated using the behavior patterns measured by the one or moresensors. An identification of the driver and the model of the driver aretransmitted to nearby vehicles with autonomous interfaces and to aremote server to store the generated model.

Additional features and advantages are realized through the techniquesof the present invention. Other embodiments and aspects of the inventionare described in detail herein and are considered a part of the claimedinvention. For a better understanding of the invention with theadvantages and the features, refer to the description and to thedrawings.

BRIEF DESCRIPTION OF THE SEVERAL VIEWS OF THE DRAWINGS

The subject matter which is regarded as the invention is particularlypointed out and distinctly claimed in the claims at the conclusion ofthe specification. The forgoing and other features, and advantages ofthe invention are apparent from the following detailed description takenin conjunction with the accompanying drawings in which:

FIG. 1 depicts a block diagram of a computer system according to anembodiment of the present invention;

FIG. 2 depicts an automatic driver modeling system according to anembodiment of the present invention;

FIG. 3 depicts a flow diagram of a driver modeling operation forintegrating human-controlled vehicles into an autonomous vehicle networkaccording to an embodiment of the present invention; and

FIG. 4 depicts scenarios for the integration of human-controlledvehicles into an autonomous vehicle network according to an embodimentof the present invention.

DETAILED DESCRIPTION

Embodiments disclosed herein integrate human-controlled vehicles into anautonomous vehicle network through automatic modeling of a human driver.When an autonomous vehicle encounters a human-controlled vehicle, it islikely that the human-controlled vehicle will not have standardautonomous interfaces. Accordingly, the human-controlled vehicle islargely an unknown to the autonomous vehicle. The autonomous vehicle isthen limited to reacting to the human-controlled vehicle in real timebased on measurements of the vehicle in real time.

Embodiments of the present invention identify the driver of thehuman-controlled vehicle based on behavior patterns of the drivermeasured by one or more sensors of the autonomous vehicle and generate amodel of the driver based on the measured behavior patterns. The modelof the driver is then transmitted from the autonomous vehicle to nearbyvehicles with autonomous interfaces and a remote central server tomaintain efficient and effective autonomous vehicle transportationnetworks that can leverage the full advantages of vehicle-to-vehicle andvehicle-to-central server communication networks.

Referring now to FIG. 1, a block diagram of a computer system 10suitable for integrating human-controlled vehicles into an autonomousvehicle network through automatic driver modeling according to exemplaryembodiments is shown. Computer system 10 is only one example of acomputer system and is not intended to suggest any limitation as to thescope of use or functionality of embodiments described herein.Regardless, computer system 10 is capable of being implemented and/orperforming any of the functionality set forth hereinabove.

Computer system 10 is operational with numerous other general purpose orspecial purpose computing system environments or configurations.Examples of well-known computing systems, environments, and/orconfigurations that may be suitable for use with computer system 10include, but are not limited to, personal computer systems, servercomputer systems, thin clients, thick clients, handheld or laptopdevices, multiprocessor systems, microprocessor-based systems, set topboxes, programmable consumer electronics, network PCs, minicomputersystems, mainframe computer systems, and distributed cloud computingenvironments that include any of the above systems or devices, and thelike.

Computer system 10 may be described in the general context of computersystem-executable instructions, such as program modules, being executedby the computer system 10. Generally, program modules may includeroutines, programs, objects, components, logic, data structures, and soon that perform particular tasks or implement particular abstract datatypes. Computer system 10 may be practiced in distributed cloudcomputing environments where tasks are performed by remote processingdevices that are linked through a communications network. In adistributed cloud computing environment, program modules may be locatedin both local and remote computer system storage media including memorystorage devices.

As shown in FIG. 1, computer system 10 is shown in the form of ageneral-purpose computing device. The components of computer system mayinclude, but are not limited to, one or more processors or processingunits 16, a system memory 28, and a bus 18 that couples various systemcomponents including system memory 28 to processor 16.

Bus 18 represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

Computer system 10 may include a variety of computer system readablemedia. Such media may be any available media that is accessible bycomputer system/server 10, and it includes both volatile andnon-volatile media, removable and non-removable media.

System memory 28 can include computer system readable media in the formof volatile memory, such as random access memory (RAM) 30 and/or cachememory 32. Computer system 10 may further include otherremovable/non-removable, volatile/non-volatile computer system storagemedia. By way of example only, storage system 34 can be provided forreading from and writing to a non-removable, non-volatile magnetic media(not shown and typically called a “hard drive”). Although not shown, amagnetic disk drive for reading from and writing to a removable,non-volatile magnetic disk (e.g., a “floppy disk”), and an optical diskdrive for reading from or writing to a removable, non-volatile opticaldisk such as a CD-ROM, DVD-ROM or other optical media can be provided.In such instances, each can be connected to bus 18 by one or more datamedia interfaces. As will be further depicted and described below,memory 28 may include at least one program product having a set (e.g.,at least one) of program modules that are configured to carry out thefunctions of embodiments of the disclosure.

Program/utility 40, having a set (at least one) of program modules 42,may be stored in memory 28 by way of example, and not limitation, aswell as an operating system, one or more application programs, otherprogram modules, and program data. Each of the operating system, one ormore application programs, other program modules, and program data orsome combination thereof, may include an implementation of a networkingenvironment. Program modules 42 generally carry out the functions and/ormethodologies of embodiments of the invention as described herein.

Computer system 10 may also communicate with one or more externaldevices 14 such as a keyboard, a pointing device, a display 24, etc.;one or more devices that enable a user to interact with computersystem/server 10; and/or any devices (e.g., network card, modem, etc.)that enable computer system/server 10 to communicate with one or moreother computing devices. Such communication can occur via Input/Output(I/O) interfaces 22. Still yet, computer system 10 can communicate withone or more networks such as a local area network (LAN), a general widearea network (WAN), and/or a public network (e.g., the Internet) vianetwork adapter 20. As depicted, network adapter 20 communicates withthe other components of computer system 10 via bus 18. It should beunderstood that although not shown, other hardware and/or softwarecomponents could be used in conjunction with computer system 10.Examples include, but are not limited to: microcode, device drivers,redundant processing units, external disk drive arrays, RAID systems,tape drives, and data archival storage systems, etc.

FIG. 2 illustrates an automatic driver modeling system 200 according toan embodiment of the present invention. The automatic driver modelingsystem 200 includes both software and hardware for sensing andcollecting data about vehicles and transmitting the data among vehicleson the roadway. The automatic driver modeling system 200 comprises anautonomous vehicle 210 and a remote server 220.

The autonomous vehicle 210 comprises components for autonomousinterfacing including, but not limited to, sensors 230, an onboardprocessing device 240, a wireless transmitter 250, and a cellularantenna 260. The sensors 230 observe and measure a behavior pattern of adriver of a human-controlled vehicle in the vicinity of the autonomousvehicle. The sensors 230 may include, but are not limited to, opticalsensors such as cameras and audio sensors such as sonar and/or radar.These sensors are used to measure and compute a vehicle's speed,acceleration, directional changes, brake frequency, movements, andvariations in the speed over a predetermined time.

The onboard processing device 240 includes an onboard data storecontaining data and models of individual drivers, and data and models ofidentified types and sub types of drivers. The onboard processing device240 may be implemented on processing unit 16 in FIG. 1. The onboardprocessing device 240 performs the tasks of coordinating the interactionbetween sensors 230 to detect and acquire data about other vehicles andto identify the vehicles when possible, interacting with the remoteserver 220 to retrieve, update, and add a driver model, building andsimulating a driver model of a driver by basing its computation onmeasurements from the sensors 230, and transmitting driver models anddata to nearby vehicles with autonomous interfaces and to the remoteserver 220. The remote server 220 stores the driver data and models andperforms processing upon the driver data and models. The remote servermay be implemented on processing unit 16 of FIG. 1.

The wireless transmitter 250 may be used to communicate informationincluding, but not limited to, driver data and models with nearbyvehicles with autonomous interfaces. The cellular antenna 260 is used toconnect to a cellular network for connecting to the remote server 220.

With reference to FIG. 3, a flow diagram of a driver modeling operation300 for integrating human-controlled vehicles into an autonomous vehiclenetwork according to an embodiment is shown. In block 310, an autonomousvehicle probes a nearby vehicle on the roadway for the presence ofautonomous interfaces. The autonomous interfaces serve to communicatevehicle parameters such as a vehicle's location, speed, and subsequentactions to the autonomous vehicle network, including autonomous vehiclesand the remote server 220. If no autonomous interface is detected, thedriver modeling operation 300 determines that the vehicle ishuman-controlled.

In block 320, a driver of a human-controlled vehicle is identified basedon behavior patterns of the driver measured by the one or more sensors230 of an autonomous vehicle 210. The autonomous vehicle 210 takesmeasurements of the behavior of the driver of the human-controlledvehicle and inputs these measurements to the onboard data store of theonboard processing device 240 for identification and modeling of thedriver. According to an embodiment, the measurements may be relayed to aremote server 220 for similar identification and modeling.

The identification of the driver of the human-controlled vehicle mayfurther include, but is not limited to, interrogating thehuman-controlled vehicle for driver identification data, analyzingregistration information of the human-controlled vehicle, andrecognizing the driver through a pattern matching algorithm. In otherwords, in addition to identifying the driver based on measured behaviorpattern, the driver may be identified based on a communicated driveridentifier or the identification of the driver may be narrowed based onthe vehicle's registration number. Additionally, identification of thedriver may occur rapidly based on known machine learning and patternmatching algorithms. For example, it is known in the art that patternsof human gait, facial recognition, and web surfing have been used touniquely identify individual humans.

In block 330, a model of the driver is generated in real-time based onthe behavior patterns of the driver of the human-controlled vehicle.This model may be as simple as estimates of the driver's reaction timesunder a variety of conditions, or as complex as predictions of adriver's next behavior based on Bayesian inference. According to anembodiment, the model of the driver allows vehicles with autonomousinterfaces to anticipate the actions of the driver. The model of thedriver may be generated using an onboard processing device 240 of theautonomous vehicle 210 or using a processing device of the remote server220. According to an embodiment, the model of the driver is continuallyupdated and augmented with new measurements of the driver behavior.

In block 340, previously stored behavior patterns of the driver areretrieved to augment the model of the driver. Measurements of thehuman-controlled vehicle, including a direct interrogation for humandriver identification data, are analyzed and used to create a unique keyor identifier for accessing additional data about the driver from theremote server 260. The retrieving of previously stored behavior patternsof the driver further comprises accessing the onboard data store or theremote server 220 using the unique identifier of the driver. The onboardprocessing device 240 of an embodiment may upload recently measureddriver data, including behavior patterns of the driver, to the remoteserver 220 to maintain a dynamically updated model of the driver in theremote server 220.

The previously stored, driver-specific data stored in the remote server220 is accessed with the unique driver identifier and is used to augmentthe parameterization of a remote or onboard model of the driver of thehuman-controlled vehicle. According to an embodiment, the autonomousvehicle 210 may use the remote server 220 to rapidly communicatehistorical data about a driver, thus allowing the model to be createdmuch more rapidly than by observation of the driver's behavior alone.According to another embodiment, the entire model of the driver, whichincludes previously stored driver behavior data, may be downloaded tothe onboard processing device 240 from the remote server 220. The remoteserver 220 may be accessed through a cellular antenna onboard theautonomous vehicle. According to another embodiment, the driver datastored in the remote server 220, including the behavior patterns of thedriver, is accessible via the Internet using the unique driveridentifier.

In block 350, the model of the driver is transmitted from the autonomousvehicle to nearby vehicles with autonomous interfaces and to the remoteserver 220 to maintain a dynamically updated model of the driver. Thetransmitted onboard or remote models of driver provide data about thehuman driver through autonomous interfaces to other autonomous vehiclesallowing them to interact with the human-controlled vehicle as if itwere autonomous and anticipate the driver's actions. The model of thedriver is transmitted from the autonomous vehicle to nearby vehicleswith autonomous interfaces through a wireless antenna onboard theautonomous vehicle 210.

According to an embodiment, the identification of the human driver doesnot depend on the human driving the same vehicle at all times, thoughthe model may be further parameterized by the type of vehicle theidentified driver is driving when encountered by the autonomous vehicle.For example, a remote server using a key generated from measurementsobserved by local autonomous vehicles may identify a rental car driveron a business trip to a new location. The model of the driver in therental car is then rapidly created and simulated and is transmitted tonearby vehicles with autonomous interfaces.

Referring to FIG. 4, scenarios for the integration of human-controlledvehicles into an autonomous vehicle network according to an embodimentare illustrated. FIG. 4 illustrates a section of a roadway on anautonomous roadway network where vehicles A, B, and C are autonomous andincorporate an embodiment of the aforementioned invention. Vehicle D ishuman-controlled and without autonomous interfaces. In the exampledepicted, it is initially assumed in scenario 1 that Vehicle A is theclosest vehicle to Vehicle D, Vehicle B is in front of Vehicle A, andVehicle C is behind Vehicle A.

In scenario 1, when Vehicle A is within sensing distance of Vehicle D,Vehicle A begins to measure data on the driving behavior pattern andidentification of Vehicle D. This data is used to create a model of thedriving behavior patterns of Vehicle D. The model of Vehicle D iscontinuously updated and compared against previously stored models ofthe driver, so as to extrapolate a more complete model of the driver'sbehavior patterns. Information on Vehicle D, such as a registrationidentifier, may be used in conjunction with observations on driverbehavior to identify the driver of the Vehicle D.

Vehicle A acquires the identity of the driver in Vehicle D and sharesthe model that Vehicle A has created of the driver of Vehicle D with aremote facility and nearby vehicles C and D. As shown in scenario 2,Vehicle A transmits the model to

Vehicle C via wireless antenna when Vehicle C is within range of VehicleA. The same operation is repeated when Vehicle B is within range ofVehicle A. Accordingly, when Vehicle D is within sensing and measuringrange of Vehicles B and C in the future, Vehicles B and C will benefitfrom the model of the driver transmitted by Vehicle A, as shown inscenario 3.

For instance, if one attribute of the model of the driver of Vehicle D'sindicated that the driver was likely to exhibit damped reactions inrelation to the behavior of other vehicles, this information could beused by Vehicle C to increase the standard distance behind Vehicle Dwhen the detected driver identity of the vehicle is the same as that inVehicle D. Vehicle C continues sharing the information it received aboutthe driver of Vehicle D with other nearby vehicles. This allows for amore robust solution, for example, in the case of lack of connectivitywith the remote facility.

According to another embodiment, a human-controlled vehicle may beequipped with an onboard processor, the onboard processor executing themethod comprising measuring behavior patterns of a driver of thehuman-controlled vehicle using one or more sensors coupled to theonboard processor, generating a dynamically updated model of the driverusing the behavior patterns measured by the one or more sensors, andtransmitting an identification of the driver and the model of the driverto nearby vehicles with autonomous interfaces and to a remote server tostore the generated model.

In this embodiment, human-controlled vehicles may be instrumented withan onboard processor device, which taps into onboard vehicle sensors tocontinually update a dynamic model of the driver under variousconditions. Accordingly, the human driver's identification and model istransmitted to nearby autonomous vehicles and remote servers. Bycarrying such a device onboard, the driver may be afforded specialdriving privileges by a regulatory body, such as the ability to accesscertain roadways dedicated primarily to autonomous vehicles, to paydecreased tolls, or any other incentives provided by a regulatory bodyto make the roadways more efficient.

The onboard processor device takes inputs from vehicle measurements,such as steering, speed, braking, and the like, and may combine themwith measurements relayed from nearby autonomous vehicles to computemodel parameters such as reaction times under a variety of roadconditions. These measurements and correlations are then stored in alocal database and modeling and simulation module. Onboard and relayedmeasurements can thereby be used to dynamically update a model of thedriver. The driver model is then used to transmit autonomous interfacesto autonomous vehicles or remote servers on certain roadways.

The model of the driver may be updated continually, thus allowing abetter model to be created and relayed to nearby vehicles that takesinto account recent driver behavior, which may be affected by thecurrent state of the diver (e.g., sleepy, agitated, in a hurry).According to an embodiment, the onboard processor device may includeprivacy controls.

As will be appreciated by one skilled in the art, aspects of the presentinvention may be embodied as a system, method or computer programproduct. Accordingly, aspects of the present invention may take the formof an entirely hardware embodiment, an entirely software embodiment(including firmware, resident software, micro-code, etc.) or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit,” “module” or “system.”Furthermore, aspects of the present invention may take the form of acomputer program product embodied in one or more computer readablemedium(s) having computer readable program code embodied thereon.

Any combination of one or more computer readable medium(s) may beutilized. The computer readable medium may be a computer readablestorage medium. A computer readable storage medium may be, for example,but not limited to, an electronic, magnetic, optical, electromagnetic,infrared, or semiconductor system, apparatus, or device, or any suitablecombination of the foregoing. More specific examples (a non-exhaustivelist) of the computer readable storage medium would include thefollowing: an electrical connection having one or more wires, a portablecomputer diskette, a hard disk, a random access memory (RAM), aread-only memory (ROM), an erasable programmable read-only memory (EPROMor Flash memory), an optical fiber, a portable compact disc read-onlymemory (CD-ROM), an optical storage device, a magnetic storage device,or any suitable combination of the foregoing. In the context of thisdocument, a computer readable storage medium may be any tangible mediumthat can contain, or store a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, RF, etc., or any suitable combination ofthe foregoing.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object oriented programming languagesuch as Java, Smalltalk, C++ or the like and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. The program code may execute entirely on theuser's computer, partly on the user's computer, as a stand-alonesoftware package, partly on the user's computer and partly on a remotecomputer or entirely on the remote computer or server. In the latterscenario, the remote computer may be connected to the user's computerthrough any type of network, including a local area network (LAN) or awide area network (WAN), or the connection may be made to an externalcomputer (for example, through the Internet using an Internet ServiceProvider).

Aspects of the present invention are described above with reference toflowchart illustrations and/or block diagrams of methods, apparatus(systems) and computer program products according to embodiments of theinvention. It will be understood that each block of the flowchartillustrations and/or block diagrams, and combinations of blocks in theflowchart illustrations and/or block diagrams, can be implemented bycomputer program instructions. These computer program instructions maybe provided to a processor of a general purpose computer, specialpurpose computer, or other programmable data processing apparatus toproduce a machine, such that the instructions, which execute via theprocessor of the computer or other programmable data processingapparatus, create means for implementing the functions/acts specified inthe flowchart and/or block diagram block or blocks.

These computer program instructions may also be stored in a computerreadable medium that can direct a computer, other programmable dataprocessing apparatus, or other devices to function in a particularmanner, such that the instructions stored in the computer readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices to causea series of operational steps to be performed on the computer, otherprogrammable apparatus or other devices to produce a computerimplemented process such that the instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The flowchart and block diagrams in the figures illustrate thearchitecture, functionality, and operation of possible implementationsof systems, methods and computer program products according to variousembodiments of the present invention. In this regard, each block in theflowchart or block diagrams may represent a module, segment, or portionof code, which comprises one or more executable instructions forimplementing the specified logical function(s). It should also be notedthat, in some alternative implementations, the functions noted in theblock may occur out of the order noted in the figures. For example, twoblocks shown in succession may, in fact, be executed substantiallyconcurrently, or the blocks may sometimes be executed in the reverseorder, depending upon the functionality involved. It will also be notedthat each block of the block diagrams and/or flowchart illustration, andcombinations of blocks in the block diagrams and/or flowchartillustration, can be implemented by special purpose hardware-basedsystems that perform the specified functions or acts, or combinations ofspecial purpose hardware and computer instructions.

The terminology used herein is for the purpose of describing particularembodiments only and is not intended to be limiting of the invention. Asused herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, element components,and/or groups thereof

The corresponding structures, materials, acts, and equivalents of allmeans or step plus function elements in the claims below are intended toinclude any structure, material, or act for performing the function incombination with other claimed elements as specifically claimed. Thedescription of the present invention has been presented for purposes ofillustration and description, but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope and spirit of the invention. Theembodiment was chosen and described in order to best explain theprinciples of the invention and the practical application, and to enableothers of ordinary skill in the art to understand the invention forvarious embodiments with various modifications as are suited to theparticular use contemplated.

The flow diagrams depicted herein are just one example. There may bemany variations to this diagram or the steps (or operations) describedtherein without departing from the spirit of the invention. Forinstance, the steps may be performed in a differing order or steps maybe added, deleted or modified. All of these variations are considered apart of the claimed invention.

While the preferred embodiment to the invention had been described, itwill be understood that those skilled in the art, both now and in thefuture, may make various improvements and enhancements which fall withinthe scope of the claims which follow. These claims should be construedto maintain the proper protection for the invention first described.

What is claimed is:
 1. A computer system, comprising: a processor, asystem memory, and a bus that couples various system componentsincluding the system memory to the processor, the system configured toperform a method comprising: driving a first autonomous vehicle;receiving, by the processor, a model of a driver of a human-controlledvehicle, wherein the model is based on behavior patterns of the drivermeasured by one or more sensors of a second autonomous vehicle, whereinthe second autonomous vehicle identifies the driver of thehuman-controlled vehicle by: interrogating the human-controlled vehiclefor driver identification data, receiving the driver identification datain response, analyzing registration information of the human-controlledvehicle, and identifying the driver by using a pattern matchingalgorithm; and generating a model of the driver of the human-controlledvehicle based on behavior patterns of the driver measured by the one ormore sensors of the second autonomous vehicle; and determining that thehuman-controlled vehicle is approaching the first autonomous vehicle,and in response altering driving of the first autonomous vehicle basedon the model of the driver of the human-controlled vehicle.
 2. Thecomputer system of claim 1, wherein the first autonomous vehicle probesa nearby vehicle on a roadway for the presence of autonomous interfaces,the autonomous interfaces communicating vehicle parameters comprisingthe nearby vehicle's location, the nearby vehicle's speed, and asubsequent action of the nearby vehicle.
 3. The computer system of claim1, wherein the model of the driver of the human-controlled vehiclefacilitates the first autonomous vehicle to anticipate an action of thedriver.
 4. The computer system of claim 1, wherein the method furthercomprises retrieving previously stored behavior patterns of the driverof the human-controlled vehicle, based on the identification of thedriver, to augment the received model of the driver from the secondautonomous vehicle.
 5. The computer system of claim 4, wherein theretrieving of previously stored behavior patterns of the driver furthercomprises: accessing a remote server using a unique identifier of thedriver; and retrieving recently measured behavior patterns of the driverfrom the remote server.
 6. The computer system of claim 4, wherein themethod further includes augmenting the model of the driver using thepreviously stored behavior patterns.
 7. The computer system of claim 6,wherein the method further comprises transmitting the augmented model toa third autonomous vehicle.
 8. The computer system of claim 1, whereinthe one or more sensors of the second autonomous vehicle compriseoptical sensors and audio sensors for measuring the behavior patterns ofthe driver, the behavior patterns including a vehicle measurementselected from a group comprising a speed, an acceleration, a directionalchange, a movement, a brake frequency, and a variation in speed over apredetermined time.
 9. The computer system of claim 7, wherein thetransmitting of the model of the driver from the autonomous vehicle tonearby vehicles with autonomous interfaces is implemented through awireless antenna onboard the autonomous vehicle.
 10. The computer systemof claim 5, wherein the accessing of the remote server is implementedthrough a cellular antenna onboard the autonomous vehicle.
 11. Acomputer program product aboard a first autonomous vehicle, the computerprogram product comprising non-transitory computer readable memory withcomputer executable instructions embedded therein, the computerexecutable instructions configured to perform a method comprising:driving the first autonomous vehicle; receiving a model of a driver of ahuman-controlled vehicle, wherein the model is based on behaviorpatterns of the driver measured by one or more sensors of a secondautonomous vehicle, wherein the second autonomous vehicle identifies thedriver of the human-controlled vehicle by:; interrogating thehuman-controlled vehicle for driver identification data, receiving thedriver identification data in response, analyzing registrationinformation of the human-controlled vehicle, and identifying the driverby using a pattern matching algorithm; and generating a model of thedriver of the human-controlled vehicle based on behavior patterns of thedriver measured by the one or more sensors of the second autonomousvehicle; and determining that the human-controlled vehicle isapproaching the first autonomous vehicle, and in response alteringdriving of the first autonomous vehicle based on the model of the driverof the human-controlled vehicle.
 12. The computer program product ofclaim 11, wherein the first autonomous vehicle probes a nearby vehicleon a roadway for the presence of autonomous interfaces, the autonomousinterfaces communicating vehicle parameters comprising the nearbyvehicle's location, the nearby vehicle's speed, and a subsequent actionof the nearby vehicle.
 13. The computer program product of claim 11,wherein the model of the driver of the human-controlled vehiclefacilitates the first autonomous vehicle to anticipate an action of thedriver.
 14. The computer program product of claim 11, wherein the methodfurther comprises retrieving previously stored behavior patterns of thedriver of the human-controlled vehicle, based on the identification ofthe driver, to augment the received model of the driver from the secondautonomous vehicle.
 15. The computer program product of claim 14,wherein the method further includes augmenting the model of the driverusing the previously stored behavior patterns.
 16. The computer programproduct of claim 15, wherein the method further comprises transmittingthe augmented model to a third autonomous vehicle.
 17. A computerimplemented method for driving a first autonomous vehicle, the methodcomprising: driving the first autonomous vehicle; receiving a model of adriver of a human-controlled vehicle, wherein the model is based onbehavior patterns of the driver measured by one or more sensors of asecond autonomous vehicle, wherein the second autonomous vehicleidentifies the driver of the human-controlled vehicle by:; interrogatingthe human-controlled vehicle for driver identification data, receivingthe driver identification data in response, analyzing registrationinformation of the human-controlled vehicle, and identifying the driverby using a pattern matching algorithm; and generating a model of thedriver of the human-controlled vehicle based on behavior patterns of thedriver measured by the one or more sensors of the second autonomousvehicle; and determining that the human-controlled vehicle isapproaching the first autonomous vehicle, and in response alteringdriving of the first autonomous vehicle based on the model of the driverof the human-controlled vehicle.
 18. The computer implemented method ofclaim 17, wherein the first autonomous vehicle probes a nearby vehicleon a roadway for the presence of autonomous interfaces, the autonomousinterfaces communicating vehicle parameters comprising the nearbyvehicle's location, the nearby vehicle's speed, and a subsequent actionof the nearby vehicle.
 19. The computer implemented method of claim 17,wherein the model of the driver of the human-controlled vehiclefacilitates the first autonomous vehicle to anticipate an action of thedriver.
 20. The computer implemented method of claim 17, wherein themethod further comprises: augmenting the model of the driver usingpreviously stored behavior patterns; and transmitting the augmentedmodel to a third autonomous vehicle.